Comment by ignoramous | Hacker News Reader

Comment by ignoramous | Hacker News Reader

Parent Full thread

> You bought a 48-pack of AA batteries? Your recommendations for the next few weeks will only be batteries, since you are clearly a battery collector!"

Patrick explained it why it might be like so: https://mobile.twitter.com/patio11/status/982208307057246209 (thread: https://threadreaderapp.com/thread/982208307057246209.html )

> I don't remember ever looking at their recommendations and finding them useful.

Amazon does various forms of recommendations (no way limited to just recent purchases) based on

1. Purchases

2. Searches

3. Browsing history (via trackers, ads, affiliate links)

4. Activities (on their digital devices, tagging things on their websites in wishlist, adding preferences in your account etc)

5. Global and Local trends (people in your area/country buy..., people with similar buying pattern buy..., It's Eid and you're Muslim, so you buy...)

...And probably many other signals I might be missing.

They must have got pretty good people working with this and enough data to get their models right.

Also, Amazon makes decisions on new retail businesses to start depending on those signals. https://techcrunch.com/2016/11/03/amazons-private-label-bran...

That, and considering the fact that Amazon has an unshakeable culture of making data-driven decisions for everything it does...

I guess what I'm trying to say is, Amazon and its subsidiaries may not be as dumb at AI/ML as it might seem to be by gauging against one datapoint.

Also see: https://www.cnbc.com/2014/04/09/big-data-knows-youre-pregnan...

Patrick's explanation is that because of buyer's remorse, and because if a thing is going to break, it often breaks early in its expected lifetime, people are more likely to buy an item (e.g. a refrigerator) soon after buying another one than they are to buy the item in general.

Okay, that makes sense to me. They go from 0.02% likelihood of wanting to buy a fridge to, let's say, 2% likelihood. A marvelous leap!

But even though they are now much more likely to buy another fridge than the average person ... I would think that they would be more likely still to buy other types of products than to buy another refrigerator.

Even if their likelihood of needing to buy more consumable products (e.g. deodorant) is only 5%, that's still double the likelihood of their likelihood to buy a fridge again.

So I would think that there would be some bias toward more frequently purchased items, even if your likelihood of buying a less frequently purchased item does indeed increase after purchase.

I'm sure the math checks out somehow, and that the creators of the recommendations algorithm wouldn't be pursuing strategies that don't work, but I don't think Patrick's explanation fully captures it.

The revenue on the refrigerator to Amazon is 10x or more higher than the deodorant.

That seems to miss the fact that if you buy a fridge on Amazon and decide to return it, you are most likely going to look for another fridge on Amazon regardless of whether Amazon puts fridges in the recommendation section or not, since you already know you need a fridge and that Amazon sells fridges.

My only explanations are that that kind of thing happens when they can't figure out anything good to recommend.

> They must have got pretty good people working with this and enough data to get their models right.

You seem to be taking this an an axiom and running with it. My impression too, is that the recommendations are pretty shoddy.

I worked previously at a recommendation systems company where we also generated this sort of recommendation. It looked incredibly shoddy to us too, but what we found out was:

- yes, people who just bought a lot of batteries are actually more likely to buy more batteries soon. same for people who just bought a fridge.

- these recommendations may cause some sort of "burn out", where people stop looking, responding, or decide to unsubscribe. But this wasn't much worse than other recommendations. including the awesome ones.

- the loss from burnout was greatly outpaced by the wins in actually targeting the right users

Yes, these are bottom-of-the-barrel recommendations. But they work. And people don't care that much even if they disagree. Maths said so.

I think this is a similar story to the complains on non-literal searches in search engines. People think they want one thing, but they want something else.

They should.

Perhaps they are not.

Perhaps the best they can do, is to recommend to all of us the same/similiar product again because thats the only thing they got working: Catching those x percent of people who send the tv back and buy the second recommended one.

Reply on news.ycombinator.com